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<title>Replication Codes for Does AI help humans make better decisions?</title>

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<h1 class="title toc-ignore">Replication Codes for
<code>Does AI help humans make better decisions?</code></h1>
<h4 class="author">Sooahn Shin</h4>



<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(aihuman)</span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">library</span>(tidyr)</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a><span class="do">## set default ggplot theme</span></span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="fu">theme_set</span>(<span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">15</span>) <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>)))</span></code></pre></div>
<p>Ben-Michael, Eli, D. James Greiner, Melody Huang, Kosuke Imai,
Zhichao Jiang, and Sooahn Shin. <a href="https://doi.org/10.1073/pnas.2505106122">“Does AI help humans make
better decisions?: A statistical evaluation framework for experimental
and observational studies”</a>, Proceedings of the National Academy of
Sciences, 2025.</p>
<p>The data used for this paper, and made available here, are interim,
based on only half of the observations in the study and (for those
observations) only half of the study follow-up period. We use them only
to illustrate methods, not to draw substantive conclusions.</p>
<p>This vignette includes replication code for the main results in the
paper as well as for Section S13 (Additional Empirical Results) in the
Supporting Information.</p>
<p>Replication data and additional helper code are included in <a href="https://cran.r-project.org/package=aihuman"><code>aihuman</code></a>
package (available on CRAN; source file is also included in the
repository), except for the following two datasets, which are provided
in the repository:</p>
<ul>
<li><code>nuis_llama</code> and <code>nuis_llama_ai</code>: nuisance
functions for Llama3 recommendations (used in the Supporting
Information, Fig. S6; see the Nuisance functions and Alternative AI
Recommendations sections below for details).</li>
</ul>
<div id="overview" class="section level2">
<h2>Overview</h2>
<p>In this vignette, we will use the data originally from <a href="https://doi.org/10.1093/jrsssa/qnad010">Imai, Jiang, Greiner,
Halen, and Shin (2023)</a>. The essential part of the data is:</p>
<ul>
<li><code>Z</code>: A binary treatment <span class="math inline">\(Z_i
\in \{0,1\}\)</span> (e.g. PSA provision)</li>
<li><code>D</code>: A binary decision <span class="math inline">\(D_i
\in \{0,1\}\)</span> (e.g. judge’s release decision)</li>
<li><code>A</code>: A binary AI recommendation <span class="math inline">\(A_i \in \{0,1\}\)</span> (e.g. dichotomized
pretrial public safety assessment scores)</li>
<li><code>Y</code>: A binary outcome <span class="math inline">\(Y_i \in
\{0,1\}\)</span> (e.g. new criminal activity)</li>
<li>Pre-treatment covariates <span class="math inline">\(X_i\)</span>
(optional)</li>
</ul>
<p>The analysis can be conducted based on the following workflow:</p>
<ol style="list-style-type: decimal">
<li>Data preparation and descriptive analysis</li>
<li>Human+AI v. Human comparison</li>
<li>AI v. Human comparison</li>
<li>Different loss functions</li>
<li>Policy Learning</li>
</ol>
</div>
<div id="data-preparation-descriptive-analysis" class="section level2">
<h2>Data Preparation &amp; Descriptive Analysis</h2>
<p>We will be using the following data:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># randomized PSA provision (0: none, 1: provided)</span></span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a>Z <span class="ot">&lt;-</span> aihuman<span class="sc">::</span>NCAdata<span class="sc">$</span>Z</span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a><span class="co"># judge&#39;s release decision (0: signature, 1: cash)</span></span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a>D <span class="ot">&lt;-</span> <span class="fu">if_else</span>(aihuman<span class="sc">::</span>NCAdata<span class="sc">$</span>D <span class="sc">==</span> <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">1</span>)</span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a><span class="co"># dichotomized pretrial public safety assessment scores (0: signature, 1: cash)</span></span>
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a>A <span class="ot">&lt;-</span> aihuman<span class="sc">::</span>PSAdata<span class="sc">$</span>DMF</span>
<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a><span class="co"># new criminal activity (0: no, 1: yes)</span></span>
<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a>Y <span class="ot">&lt;-</span> aihuman<span class="sc">::</span>NCAdata<span class="sc">$</span>Y</span></code></pre></div>
<p>For subgroup analysis, we will use the following covariates:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># race</span></span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a>race_vec <span class="ot">&lt;-</span> aihuman<span class="sc">::</span>NCAdata <span class="sc">|&gt;</span> </span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">race =</span> <span class="fu">if_else</span>(White <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Non-white&quot;</span>)) <span class="sc">|&gt;</span></span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a>  <span class="fu">pull</span>(race)</span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># gender</span></span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a>gender_vec <span class="ot">&lt;-</span> aihuman<span class="sc">::</span>NCAdata <span class="sc">|&gt;</span> </span>
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">gender =</span> <span class="fu">if_else</span>(Sex <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Male&quot;</span>, <span class="st">&quot;Female&quot;</span>)) <span class="sc">|&gt;</span></span>
<span id="cb3-8"><a href="#cb3-8" tabindex="-1"></a>  <span class="fu">pull</span>(gender)</span></code></pre></div>
<div id="nuisance-functions" class="section level3">
<h3>Nuisance functions</h3>
<p>In the paper, we use doubly robust estimators throughout the
analysis. For this purpose, we will be fitting the nuisance functions
using the following covariates (<span class="math inline">\(X_i\)</span>):</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a>cov_mat <span class="ot">&lt;-</span> aihuman<span class="sc">::</span>NCAdata <span class="sc">|&gt;</span> </span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a>  <span class="fu">select</span>(<span class="sc">-</span><span class="fu">c</span>(Y, D, Z)) <span class="sc">|&gt;</span> </span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a>  <span class="fu">bind_cols</span>(<span class="at">FTAScore =</span> aihuman<span class="sc">::</span>PSAdata<span class="sc">$</span>FTAScore, </span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a>            <span class="at">NCAScore =</span> aihuman<span class="sc">::</span>PSAdata<span class="sc">$</span>NCAScore, </span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a>            <span class="at">NVCAFlag =</span> aihuman<span class="sc">::</span>PSAdata<span class="sc">$</span>NVCAFlag) <span class="sc">|&gt;</span></span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a>  <span class="fu">as.matrix</span>()</span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a><span class="fu">colnames</span>(cov_mat)</span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a><span class="co">#&gt;  [1] &quot;Sex&quot;                             &quot;White&quot;                          </span></span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a><span class="co">#&gt;  [3] &quot;SexWhite&quot;                        &quot;Age&quot;                            </span></span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;PendingChargeAtTimeOfOffense&quot;    &quot;NCorNonViolentMisdemeanorCharge&quot;</span></span>
<span id="cb4-11"><a href="#cb4-11" tabindex="-1"></a><span class="co">#&gt;  [7] &quot;ViolentMisdemeanorCharge&quot;        &quot;ViolentFelonyCharge&quot;            </span></span>
<span id="cb4-12"><a href="#cb4-12" tabindex="-1"></a><span class="co">#&gt;  [9] &quot;NonViolentFelonyCharge&quot;          &quot;PriorMisdemeanorConviction&quot;     </span></span>
<span id="cb4-13"><a href="#cb4-13" tabindex="-1"></a><span class="co">#&gt; [11] &quot;PriorFelonyConviction&quot;           &quot;PriorViolentConviction&quot;         </span></span>
<span id="cb4-14"><a href="#cb4-14" tabindex="-1"></a><span class="co">#&gt; [13] &quot;PriorSentenceToIncarceration&quot;    &quot;PriorFTAInPast2Years&quot;           </span></span>
<span id="cb4-15"><a href="#cb4-15" tabindex="-1"></a><span class="co">#&gt; [15] &quot;PriorFTAOlderThan2Years&quot;         &quot;Staff_ReleaseRecommendation&quot;    </span></span>
<span id="cb4-16"><a href="#cb4-16" tabindex="-1"></a><span class="co">#&gt; [17] &quot;FTAScore&quot;                        &quot;NCAScore&quot;                       </span></span>
<span id="cb4-17"><a href="#cb4-17" tabindex="-1"></a><span class="co">#&gt; [19] &quot;NVCAFlag&quot;</span></span></code></pre></div>
<p>Then, the first set of the nuisance functions can be computed as
follows, based on the Generalized Boosted Regression Modeling (see
<code>gbm::gbm</code> function):</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>nuis_func <span class="ot">&lt;-</span> <span class="fu">compute_nuisance_functions</span>(<span class="at">Y =</span> Y, <span class="at">D =</span> D, <span class="at">Z =</span> Z, <span class="at">V =</span> cov_mat, <span class="at">shrinkage =</span> <span class="fl">0.01</span>, <span class="at">n.trees =</span> <span class="dv">1000</span>)</span></code></pre></div>
<p>This gives us the following nuisance functions:</p>
<ul>
<li>The decision model <span class="math inline">\(m^{D}(z, x) := \Pr(D
= 1 \mid Z = z, X = x)\)</span>, for each treatment group <span class="math inline">\(z \in \{0,1\}\)</span>.
<code>nuis_func$z_models$d_pred</code> gives estimated <span class="math inline">\(\hat{m}^{D}(z, X_i)\)</span>.</li>
<li>The outcome model <span class="math inline">\(m^{Y}(z, x) := \Pr(Y =
1 \mid D = 0, Z = z, X = x)\)</span>, for each treatment group <span class="math inline">\(z \in \{0,1\}\)</span>.
<code>nuis_func$z_models$y_pred</code> gives estimated <span class="math inline">\(\hat{m}^{Y}(z, X_i)\)</span>.</li>
<li>The propensity score model <span class="math inline">\(e(z, X_i) :=
\Pr(Z = z \mid X = X_i)\)</span>. <code>nuis_func$pscore</code> gives
estimated <span class="math inline">\(\hat{e}(1, X_i)\)</span>.</li>
</ul>
<p>The second set of the nuisance functions <em>conditioning on AI
recommendation</em> can be computed similarly:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>nuis_func_ai <span class="ot">&lt;-</span> <span class="fu">compute_nuisance_functions_ai</span>(<span class="at">Y =</span> Y, <span class="at">D =</span> D, <span class="at">Z =</span> Z, <span class="at">A =</span> A, <span class="at">V =</span> cov_mat, <span class="at">shrinkage =</span> <span class="fl">0.01</span>, <span class="at">n.trees =</span> <span class="dv">1000</span>)</span></code></pre></div>
<p>This gives us the following nuisance functions:</p>
<ul>
<li>The decision model <span class="math inline">\(m^{D}(z, a, x) :=
\Pr(D = 1 \mid Z = z, A = a, X = x)\)</span>, for each treatment group
<span class="math inline">\(z \in \{0,1\}\)</span> and AI recommendation
<span class="math inline">\(a \in \{0,1\}\)</span>.
<code>nuis_func_ai$z_models$d_pred</code> gives estimated <span class="math inline">\(\hat{m}^{D}(z, a, X_i)\)</span>.</li>
<li>The outcome model <span class="math inline">\(m^{Y}(z, a, x) :=
\Pr(Y = 1 \mid D = 0, Z = z, A = a, X = x)\)</span>, for each treatment
group <span class="math inline">\(z \in \{0,1\}\)</span> and AI
recommendation <span class="math inline">\(a \in \{0,1\}\)</span>.
<code>nuis_func_ai$z_models$y_pred</code> gives estimated <span class="math inline">\(\hat{m}^{Y}(z, a, X_i)\)</span>.</li>
<li>The propensity score model <span class="math inline">\(e(z, X_i) :=
\Pr(Z = z \mid X = X_i)\)</span>. <code>nuis_func_ai$pscore</code> gives
estimated <span class="math inline">\(\hat{e}(1, X_i)\)</span>.</li>
</ul>
<p>For the reproducibility, we will be using the pre-computed nuisance
functions, generated from the same codes above.</p>
</div>
<div id="contingency-table-of-human-decisions-and-psa-recommendations" class="section level3">
<h3>Contingency table of human decisions and PSA recommendations</h3>
<p>Comparison between human decisions and PSA-generated recommendations
are summarized in the following contingency table (Table 1 in the
paper).</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>counts <span class="ot">&lt;-</span> <span class="fu">table</span>(D[Z <span class="sc">==</span> <span class="dv">0</span>], A[Z <span class="sc">==</span> <span class="dv">0</span>])</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>proportions <span class="ot">&lt;-</span> <span class="fu">prop.table</span>(counts) <span class="sc">*</span> <span class="dv">100</span>  </span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>combined_table_human <span class="ot">&lt;-</span> <span class="fu">matrix</span>(<span class="fu">sprintf</span>(<span class="st">&quot;%.1f%% (%d)&quot;</span>, proportions, counts),</span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>                         <span class="at">nrow =</span> <span class="fu">nrow</span>(counts), <span class="at">ncol =</span> <span class="fu">ncol</span>(counts))</span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="fu">colnames</span>(combined_table_human) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Signature&quot;</span>, <span class="st">&quot;Cash&quot;</span>)</span>
<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="fu">rownames</span>(combined_table_human) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Signature&quot;</span>, <span class="st">&quot;Cash&quot;</span>)</span>
<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(combined_table_human, <span class="at">caption =</span> <span class="st">&quot;Table 1 (Human v. PSA)&quot;</span>)</span></code></pre></div>
<table>
<caption>Table 1 (Human v. PSA)</caption>
<thead>
<tr class="header">
<th align="left"></th>
<th align="left">Signature</th>
<th align="left">Cash</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Signature</td>
<td align="left">54.1% (510)</td>
<td align="left">20.7% (195)</td>
</tr>
<tr class="even">
<td align="left">Cash</td>
<td align="left">9.4% (89)</td>
<td align="left">15.8% (149)</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>counts <span class="ot">&lt;-</span> <span class="fu">table</span>(D[Z <span class="sc">==</span> <span class="dv">1</span>], A[Z <span class="sc">==</span> <span class="dv">1</span>])</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>proportions <span class="ot">&lt;-</span> <span class="fu">prop.table</span>(counts) <span class="sc">*</span> <span class="dv">100</span>  </span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>combined_table_human_ai <span class="ot">&lt;-</span> <span class="fu">matrix</span>(<span class="fu">sprintf</span>(<span class="st">&quot;%.1f%% (%d)&quot;</span>, proportions, counts),</span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a>                         <span class="at">nrow =</span> <span class="fu">nrow</span>(counts), <span class="at">ncol =</span> <span class="fu">ncol</span>(counts))</span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="fu">colnames</span>(combined_table_human_ai) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Signature&quot;</span>, <span class="st">&quot;Cash&quot;</span>)</span>
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="fu">rownames</span>(combined_table_human_ai) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Signature&quot;</span>, <span class="st">&quot;Cash&quot;</span>)</span>
<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a>knitr<span class="sc">::</span><span class="fu">kable</span>(combined_table_human_ai, <span class="at">caption =</span> <span class="st">&quot;Table 1 (Human+PSA v. PSA)&quot;</span>)</span></code></pre></div>
<table>
<caption>Table 1 (Human+PSA v. PSA)</caption>
<thead>
<tr class="header">
<th align="left"></th>
<th align="left">Signature</th>
<th align="left">Cash</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Signature</td>
<td align="left">57.3% (543)</td>
<td align="left">17.1% (162)</td>
</tr>
<tr class="even">
<td align="left">Cash</td>
<td align="left">7.4% (70)</td>
<td align="left">18.2% (173)</td>
</tr>
</tbody>
</table>
</div>
<div id="agreement-between-human-decisions-and-psa-recommendations" class="section level3">
<h3>Agreement between human decisions and PSA recommendations</h3>
<p>We can analyze the impact of AI recommendations on agreement between
human decisions and AI recommendations using the difference in means
estimates of an indicator <span class="math inline">\(1\{D_i =
A_i\}\)</span>. The results are as follows (Figure S1 in the
appendix).</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">table_agreement</span>(</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span></span>
<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a>) <span class="sc">|&gt;</span></span>
<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a>  <span class="fu">mutate_at</span>(<span class="fu">vars</span>(agree_diff, agree_diff_se, ci_ub, ci_lb), <span class="sc">~</span><span class="fu">round</span>(., <span class="dv">3</span>)) <span class="sc">|&gt;</span></span>
<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a>  knitr<span class="sc">::</span><span class="fu">kable</span>(<span class="at">caption =</span> <span class="st">&quot;Agreement of Human and PSA Decision Makers&quot;</span>)</span></code></pre></div>
<table>
<caption>Agreement of Human and PSA Decision Makers</caption>
<thead>
<tr class="header">
<th align="left">cov</th>
<th align="left">X</th>
<th align="right">agree_diff</th>
<th align="right">agree_diff_se</th>
<th align="right">ci_ub</th>
<th align="right">ci_lb</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Overall</td>
<td align="left">Overall</td>
<td align="right">0.056</td>
<td align="right">0.020</td>
<td align="right">0.097</td>
<td align="right">0.016</td>
</tr>
<tr class="even">
<td align="left">Race</td>
<td align="left">Non-white</td>
<td align="right">0.030</td>
<td align="right">0.031</td>
<td align="right">0.090</td>
<td align="right">-0.031</td>
</tr>
<tr class="odd">
<td align="left">Race</td>
<td align="left">White</td>
<td align="right">0.079</td>
<td align="right">0.027</td>
<td align="right">0.132</td>
<td align="right">0.026</td>
</tr>
<tr class="even">
<td align="left">Gender</td>
<td align="left">Female</td>
<td align="right">0.025</td>
<td align="right">0.044</td>
<td align="right">0.111</td>
<td align="right">-0.060</td>
</tr>
<tr class="odd">
<td align="left">Gender</td>
<td align="left">Male</td>
<td align="right">0.065</td>
<td align="right">0.023</td>
<td align="right">0.110</td>
<td align="right">0.019</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="fu">plot_agreement</span>(</span>
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb10-9"><a href="#cb10-9" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb10-10"><a href="#cb10-10" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb10-11"><a href="#cb10-11" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="st">&quot;Agreement between Human Decisions and PSA Recommendations&quot;</span>, <span class="at">p.lb =</span> <span class="sc">-</span><span class="fl">0.25</span>, <span class="at">p.ub =</span> <span class="fl">0.25</span></span>
<span id="cb10-12"><a href="#cb10-12" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="humanai-v.-human-comparison" class="section level2">
<h2>Human+AI v. Human comparison</h2>
<p>We now compare difference in risk between human+AI and human decision
makers using AIPW estimators. You may choose between (1) AIPW estimator
and (2) difference-in-means (Neyman) estimator. You can also specify the
loss ratio between false positives and false negatives using
<code>l01</code> parameter. For the subgroup analysis, you can specify
the subgroup variable using <code>X</code>.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="co"># AIPW estimator</span></span>
<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a><span class="fu">compute_stats_aipw</span>(</span>
<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func, </span>
<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a>  <span class="at">X =</span> <span class="cn">NULL</span>,</span>
<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span></span>
<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a>)</span>
<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a><span class="co">#&gt; # A tibble: 1 × 8</span></span>
<span id="cb11-12"><a href="#cb11-12" tabindex="-1"></a><span class="co">#&gt;   Z_focal Z_compare loss_diff loss_diff_se fn_diff fn_diff_se fp_diff fp_diff_se</span></span>
<span id="cb11-13"><a href="#cb11-13" tabindex="-1"></a><span class="co">#&gt;     &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;        &lt;dbl&gt;   &lt;dbl&gt;      &lt;dbl&gt;   &lt;dbl&gt;      &lt;dbl&gt;</span></span>
<span id="cb11-14"><a href="#cb11-14" tabindex="-1"></a><span class="co">#&gt; 1       1         0    0.0419       0.0359  0.0177     0.0184  0.0242     0.0218</span></span>
<span id="cb11-15"><a href="#cb11-15" tabindex="-1"></a><span class="co"># Difference-in-means (Neyman) estimator</span></span>
<span id="cb11-16"><a href="#cb11-16" tabindex="-1"></a><span class="fu">compute_stats</span>(</span>
<span id="cb11-17"><a href="#cb11-17" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb11-18"><a href="#cb11-18" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb11-19"><a href="#cb11-19" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb11-20"><a href="#cb11-20" tabindex="-1"></a>  <span class="at">X =</span> <span class="cn">NULL</span>,</span>
<span id="cb11-21"><a href="#cb11-21" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span></span>
<span id="cb11-22"><a href="#cb11-22" tabindex="-1"></a>)</span>
<span id="cb11-23"><a href="#cb11-23" tabindex="-1"></a><span class="co">#&gt; # A tibble: 1 × 8</span></span>
<span id="cb11-24"><a href="#cb11-24" tabindex="-1"></a><span class="co">#&gt;   Z_focal Z_compare loss_diff loss_diff_se fn_diff fn_diff_se fp_diff fp_diff_se</span></span>
<span id="cb11-25"><a href="#cb11-25" tabindex="-1"></a><span class="co">#&gt;     &lt;dbl&gt;     &lt;dbl&gt;     &lt;dbl&gt;        &lt;dbl&gt;   &lt;dbl&gt;      &lt;dbl&gt;   &lt;dbl&gt;      &lt;dbl&gt;</span></span>
<span id="cb11-26"><a href="#cb11-26" tabindex="-1"></a><span class="co">#&gt; 1       1         0    0.0420       0.0364  0.0190     0.0184  0.0230     0.0229</span></span></code></pre></div>
<p>The results can be visualized as follows (Figure 1 in the paper). You
may use <code>plot_diff_human()</code> function for the Neyman
estimator.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="fu">plot_diff_human_aipw</span>(</span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func, </span>
<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span>,</span>
<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb12-9"><a href="#cb12-9" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb12-10"><a href="#cb12-10" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb12-11"><a href="#cb12-11" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb12-12"><a href="#cb12-12" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb12-13"><a href="#cb12-13" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="cn">NULL</span>, <span class="at">p.lb =</span> <span class="sc">-</span><span class="fl">0.3</span>, <span class="at">p.ub =</span> <span class="fl">0.3</span></span>
<span id="cb12-14"><a href="#cb12-14" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<div id="how-the-human-overrides-the-ai-recommendation" class="section level3">
<h3>How the human overrides the AI recommendation?</h3>
<p>We can answer the question using the following subgroup analysis
defined by <code>A</code>.</p>
<p>First, subgroup analysis for the cases where AI recommends harsher
decision (<span class="math inline">\(A = 1\)</span>). The figure shows
how the human overrides the AI recommendation in terms of true negative
proportion (TNP), false negative proportion (FNP), and their differences
(Figure S2 in the appendix).</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a><span class="fu">plot_diff_subgroup</span>(</span>
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a>  <span class="at">a =</span> <span class="dv">1</span>,</span>
<span id="cb13-7"><a href="#cb13-7" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span>,</span>
<span id="cb13-8"><a href="#cb13-8" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func,</span>
<span id="cb13-9"><a href="#cb13-9" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb13-10"><a href="#cb13-10" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb13-11"><a href="#cb13-11" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb13-12"><a href="#cb13-12" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb13-13"><a href="#cb13-13" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb13-14"><a href="#cb13-14" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb13-15"><a href="#cb13-15" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="cn">NULL</span>, <span class="at">p.lb =</span> <span class="sc">-</span><span class="fl">0.5</span>, <span class="at">p.ub =</span> <span class="fl">0.5</span>,</span>
<span id="cb13-16"><a href="#cb13-16" tabindex="-1"></a>  <span class="at">label =</span> <span class="st">&quot;TNP - FNP&quot;</span>,</span>
<span id="cb13-17"><a href="#cb13-17" tabindex="-1"></a>  <span class="at">metrics =</span> <span class="fu">c</span>(<span class="st">&quot;True Negative Proportion (TNP)&quot;</span>, <span class="st">&quot;False Negative Proportion (FNP)&quot;</span>, <span class="st">&quot;TNP - FNP&quot;</span>)</span>
<span id="cb13-18"><a href="#cb13-18" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>Second, subgroup analysis for the cases where AI recommends lenient
decision (<span class="math inline">\(A = 0\)</span>) is also available.
The figure shows how human overrides the AI recommendation in terms of
true positive proportion (TPP), false positive proportion (FPP), and
their differences (Figure S3 in the appendix).</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="fu">plot_diff_subgroup</span>(</span>
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a>  <span class="at">a =</span> <span class="dv">0</span>,</span>
<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span>,</span>
<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func,</span>
<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb14-13"><a href="#cb14-13" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb14-14"><a href="#cb14-14" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb14-15"><a href="#cb14-15" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="cn">NULL</span>, <span class="at">p.lb =</span> <span class="sc">-</span><span class="fl">0.5</span>, <span class="at">p.ub =</span> <span class="fl">0.5</span>,</span>
<span id="cb14-16"><a href="#cb14-16" tabindex="-1"></a>  <span class="at">label =</span> <span class="st">&quot;TPP - FPP&quot;</span>,</span>
<span id="cb14-17"><a href="#cb14-17" tabindex="-1"></a>  <span class="at">metrics =</span> <span class="fu">c</span>(<span class="st">&quot;True Positive Proportion (TPP)&quot;</span>, <span class="st">&quot;False Positive Proportion (FPP)&quot;</span>, <span class="st">&quot;TPP - FPP&quot;</span>)</span>
<span id="cb14-18"><a href="#cb14-18" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
</div>
</div>
<div id="ai-v.-human-comparison" class="section level2">
<h2>AI v. Human comparison</h2>
<p>We now compare difference in risk between AI and human decision
makers using AIPW estimators. Note that these quantities are partially
identified given the assumptions made in the paper.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="co"># AIPW estimator</span></span>
<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="fu">compute_bounds_aipw</span>(</span>
<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func, </span>
<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a>  <span class="at">nuis_funcs_ai =</span> nuis_func_ai,</span>
<span id="cb15-9"><a href="#cb15-9" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb15-10"><a href="#cb15-10" tabindex="-1"></a>  <span class="at">X =</span> <span class="cn">NULL</span>,</span>
<span id="cb15-11"><a href="#cb15-11" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span></span>
<span id="cb15-12"><a href="#cb15-12" tabindex="-1"></a>)</span>
<span id="cb15-13"><a href="#cb15-13" tabindex="-1"></a><span class="co">#&gt; # A tibble: 2 × 14</span></span>
<span id="cb15-14"><a href="#cb15-14" tabindex="-1"></a><span class="co">#&gt;   Z_focal Z_compare fn_diff_lb fn_diff_ub fp_diff_lb fp_diff_ub loss_diff_lb</span></span>
<span id="cb15-15"><a href="#cb15-15" tabindex="-1"></a><span class="co">#&gt;   &lt;chr&gt;   &lt;chr&gt;          &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;      &lt;dbl&gt;        &lt;dbl&gt;</span></span>
<span id="cb15-16"><a href="#cb15-16" tabindex="-1"></a><span class="co">#&gt; 1 AI      1            -0.0578    0.00203     0.0421      0.102      -0.0156</span></span>
<span id="cb15-17"><a href="#cb15-17" tabindex="-1"></a><span class="co">#&gt; 2 AI      0            -0.0401    0.0197      0.0674      0.127       0.0273</span></span>
<span id="cb15-18"><a href="#cb15-18" tabindex="-1"></a><span class="co">#&gt; # ℹ 7 more variables: loss_diff_ub &lt;dbl&gt;, fn_diff_lb_se &lt;dbl&gt;,</span></span>
<span id="cb15-19"><a href="#cb15-19" tabindex="-1"></a><span class="co">#&gt; #   fn_diff_ub_se &lt;dbl&gt;, fp_diff_lb_se &lt;dbl&gt;, fp_diff_ub_se &lt;dbl&gt;,</span></span>
<span id="cb15-20"><a href="#cb15-20" tabindex="-1"></a><span class="co">#&gt; #   loss_diff_lb_se &lt;dbl&gt;, loss_diff_ub_se &lt;dbl&gt;</span></span></code></pre></div>
<p>The results can be visualized as follows (Figure 2 and Figure S4 in
the paper).</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="co"># AI versus Human</span></span>
<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a><span class="fu">plot_diff_ai_aipw</span>(</span>
<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb16-7"><a href="#cb16-7" tabindex="-1"></a>  <span class="at">z_compare =</span> <span class="dv">0</span>,</span>
<span id="cb16-8"><a href="#cb16-8" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func, </span>
<span id="cb16-9"><a href="#cb16-9" tabindex="-1"></a>  <span class="at">nuis_funcs_ai =</span> nuis_func_ai,</span>
<span id="cb16-10"><a href="#cb16-10" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span>,</span>
<span id="cb16-11"><a href="#cb16-11" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb16-12"><a href="#cb16-12" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb16-13"><a href="#cb16-13" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb16-14"><a href="#cb16-14" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb16-15"><a href="#cb16-15" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb16-16"><a href="#cb16-16" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb16-17"><a href="#cb16-17" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="cn">NULL</span>, <span class="at">p.lb =</span> <span class="sc">-</span><span class="fl">0.3</span>, <span class="at">p.ub =</span> <span class="fl">0.3</span></span>
<span id="cb16-18"><a href="#cb16-18" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a></span>
<span id="cb17-2"><a href="#cb17-2" tabindex="-1"></a><span class="co"># AI versus Human+AI</span></span>
<span id="cb17-3"><a href="#cb17-3" tabindex="-1"></a><span class="fu">plot_diff_ai_aipw</span>(</span>
<span id="cb17-4"><a href="#cb17-4" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb17-5"><a href="#cb17-5" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb17-6"><a href="#cb17-6" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb17-7"><a href="#cb17-7" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb17-8"><a href="#cb17-8" tabindex="-1"></a>  <span class="at">z_compare =</span> <span class="dv">1</span>,</span>
<span id="cb17-9"><a href="#cb17-9" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func, </span>
<span id="cb17-10"><a href="#cb17-10" tabindex="-1"></a>  <span class="at">nuis_funcs_ai =</span> nuis_func_ai,</span>
<span id="cb17-11"><a href="#cb17-11" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span>,</span>
<span id="cb17-12"><a href="#cb17-12" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb17-13"><a href="#cb17-13" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb17-14"><a href="#cb17-14" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb17-15"><a href="#cb17-15" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb17-16"><a href="#cb17-16" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb17-17"><a href="#cb17-17" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb17-18"><a href="#cb17-18" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="cn">NULL</span>, <span class="at">p.lb =</span> <span class="sc">-</span><span class="fl">0.3</span>, <span class="at">p.ub =</span> <span class="fl">0.3</span></span>
<span id="cb17-19"><a href="#cb17-19" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" 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" /><!-- --></p>
<div id="alternative-ai-recommendations" class="section level3">
<h3>Alternative AI Recommendations</h3>
<p>Note that researcher can specify different AI recommendations by
changing the <code>A</code> variable. For example, in the paper, we have
used Llama3 recommendations as an alternative comparison (Figure
S6).</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a><span class="co"># Llama3 versus Human</span></span>
<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a><span class="co"># pre-computed nuisance functions for Llama3 recommendations (for replication)</span></span>
<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a><span class="co"># check &quot;Nuisance functions&quot; section for the codes to compute these nuisance functions</span></span>
<span id="cb18-4"><a href="#cb18-4" tabindex="-1"></a>nuis_llama <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;nuis_llama.rds&quot;</span>)</span>
<span id="cb18-5"><a href="#cb18-5" tabindex="-1"></a>nuis_llama_ai <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="st">&quot;nuis_llama_ai.rds&quot;</span>)</span>
<span id="cb18-6"><a href="#cb18-6" tabindex="-1"></a><span class="fu">plot_diff_ai_aipw</span>(</span>
<span id="cb18-7"><a href="#cb18-7" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb18-8"><a href="#cb18-8" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb18-9"><a href="#cb18-9" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb18-10"><a href="#cb18-10" tabindex="-1"></a>  <span class="at">A =</span> A_llama,</span>
<span id="cb18-11"><a href="#cb18-11" tabindex="-1"></a>  <span class="at">z_compare =</span> <span class="dv">0</span>,</span>
<span id="cb18-12"><a href="#cb18-12" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_llama, </span>
<span id="cb18-13"><a href="#cb18-13" tabindex="-1"></a>  <span class="at">nuis_funcs_ai =</span> nuis_llama_ai,</span>
<span id="cb18-14"><a href="#cb18-14" tabindex="-1"></a>  <span class="at">l01 =</span> <span class="dv">1</span>,</span>
<span id="cb18-15"><a href="#cb18-15" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb18-16"><a href="#cb18-16" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb18-17"><a href="#cb18-17" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb18-18"><a href="#cb18-18" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb18-19"><a href="#cb18-19" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb18-20"><a href="#cb18-20" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb18-21"><a href="#cb18-21" tabindex="-1"></a>  <span class="at">y.lab =</span> <span class="st">&quot;Llama3 versus Human&quot;</span>, <span class="at">p.label =</span> <span class="fu">c</span>(<span class="st">&quot;Llama3 worse&quot;</span>, <span class="st">&quot;Llama3 better&quot;</span>),</span>
<span id="cb18-22"><a href="#cb18-22" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="cn">NULL</span>, <span class="at">p.lb =</span> <span class="sc">-</span><span class="fl">0.5</span>, <span class="at">p.ub =</span> <span class="fl">0.5</span></span>
<span id="cb18-23"><a href="#cb18-23" tabindex="-1"></a>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
<div id="different-loss-functions" class="section level2">
<h2>Different loss functions</h2>
<p>As noted earlier, one may specify different loss functions by
changing the <code>l01</code> parameter. Here, we provide an example of
when human decision maker is preferred over AI recommendation (Figure 3
and Figure S5 in the paper).</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="co"># When to prefer human decision maker over AI recommendation</span></span>
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a><span class="fu">plot_preference</span>(</span>
<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb19-7"><a href="#cb19-7" tabindex="-1"></a>  <span class="at">z_compare =</span> <span class="dv">0</span>,</span>
<span id="cb19-8"><a href="#cb19-8" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func, </span>
<span id="cb19-9"><a href="#cb19-9" tabindex="-1"></a>  <span class="at">nuis_funcs_ai =</span> nuis_func_ai,</span>
<span id="cb19-10"><a href="#cb19-10" tabindex="-1"></a>  <span class="at">l01_seq =</span> <span class="dv">10</span><span class="sc">^</span><span class="fu">seq</span>(<span class="sc">-</span><span class="dv">2</span>, <span class="dv">2</span>, <span class="at">length.out =</span> <span class="dv">200</span>), </span>
<span id="cb19-11"><a href="#cb19-11" tabindex="-1"></a>  <span class="at">alpha =</span> <span class="fl">0.05</span>,</span>
<span id="cb19-12"><a href="#cb19-12" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb19-13"><a href="#cb19-13" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb19-14"><a href="#cb19-14" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb19-15"><a href="#cb19-15" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb19-16"><a href="#cb19-16" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb19-17"><a href="#cb19-17" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb19-18"><a href="#cb19-18" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="cn">NULL</span>, </span>
<span id="cb19-19"><a href="#cb19-19" tabindex="-1"></a>  <span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb19-20"><a href="#cb19-20" tabindex="-1"></a>  <span class="at">p.label =</span> <span class="fu">c</span>(<span class="st">&quot;AI-alone preferred&quot;</span>, <span class="st">&quot;Human-alone preferred&quot;</span>, <span class="st">&quot;Ambiguous&quot;</span>)</span>
<span id="cb19-21"><a href="#cb19-21" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb19-22"><a href="#cb19-22" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="st">&quot;&quot;</span>,</span>
<span id="cb19-23"><a href="#cb19-23" tabindex="-1"></a>                     <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;#4d9221&quot;</span>, <span class="st">&quot;grey30&quot;</span>),</span>
<span id="cb19-24"><a href="#cb19-24" tabindex="-1"></a>                     <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;Human-alone preferred&quot;</span>, <span class="st">&quot;Ambiguous&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span></span>
<span id="cb19-25"><a href="#cb19-25" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb19-26"><a href="#cb19-26" tabindex="-1"></a>  <span class="fu">scale_linetype_manual</span>(<span class="st">&quot;&quot;</span>, <span class="at">values =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">1</span>, <span class="dv">3</span>), <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;Human-alone preferred&quot;</span>, <span class="st">&quot;Ambiguous&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a><span class="co"># When to prefer human+AI decision maker over AI recommendation</span></span>
<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a><span class="fu">plot_preference</span>(</span>
<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a>  <span class="at">Y =</span> Y,</span>
<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a>  <span class="at">D =</span> D,</span>
<span id="cb20-5"><a href="#cb20-5" tabindex="-1"></a>  <span class="at">Z =</span> Z,</span>
<span id="cb20-6"><a href="#cb20-6" tabindex="-1"></a>  <span class="at">A =</span> A,</span>
<span id="cb20-7"><a href="#cb20-7" tabindex="-1"></a>  <span class="at">z_compare =</span> <span class="dv">1</span>,</span>
<span id="cb20-8"><a href="#cb20-8" tabindex="-1"></a>  <span class="at">nuis_funcs =</span> nuis_func, </span>
<span id="cb20-9"><a href="#cb20-9" tabindex="-1"></a>  <span class="at">nuis_funcs_ai =</span> nuis_func_ai,</span>
<span id="cb20-10"><a href="#cb20-10" tabindex="-1"></a>  <span class="at">l01_seq =</span> <span class="dv">10</span><span class="sc">^</span><span class="fu">seq</span>(<span class="sc">-</span><span class="dv">2</span>, <span class="dv">2</span>, <span class="at">length.out =</span> <span class="dv">200</span>), </span>
<span id="cb20-11"><a href="#cb20-11" tabindex="-1"></a>  <span class="at">alpha =</span> <span class="fl">0.05</span>,</span>
<span id="cb20-12"><a href="#cb20-12" tabindex="-1"></a>  <span class="at">true.pscore =</span> <span class="fu">rep</span>(<span class="fl">0.5</span>, <span class="fu">length</span>(D)),</span>
<span id="cb20-13"><a href="#cb20-13" tabindex="-1"></a>  <span class="at">subgroup1 =</span> race_vec,</span>
<span id="cb20-14"><a href="#cb20-14" tabindex="-1"></a>  <span class="at">subgroup2 =</span> gender_vec,</span>
<span id="cb20-15"><a href="#cb20-15" tabindex="-1"></a>  <span class="at">label.subgroup1 =</span> <span class="st">&quot;Race&quot;</span>,</span>
<span id="cb20-16"><a href="#cb20-16" tabindex="-1"></a>  <span class="at">label.subgroup2 =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb20-17"><a href="#cb20-17" tabindex="-1"></a>  <span class="at">x.order =</span> <span class="fu">c</span>(<span class="st">&quot;Overall&quot;</span>, <span class="st">&quot;Non-white&quot;</span>, <span class="st">&quot;White&quot;</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb20-18"><a href="#cb20-18" tabindex="-1"></a>  <span class="at">p.title =</span> <span class="cn">NULL</span>, </span>
<span id="cb20-19"><a href="#cb20-19" tabindex="-1"></a>  <span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>,</span>
<span id="cb20-20"><a href="#cb20-20" tabindex="-1"></a>  <span class="at">p.label =</span> <span class="fu">c</span>(<span class="st">&quot;AI-alone preferred&quot;</span>, <span class="st">&quot;Human-alone preferred&quot;</span>, <span class="st">&quot;Ambiguous&quot;</span>)</span>
<span id="cb20-21"><a href="#cb20-21" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb20-22"><a href="#cb20-22" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="st">&quot;&quot;</span>,</span>
<span id="cb20-23"><a href="#cb20-23" tabindex="-1"></a>              <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;#4d9221&quot;</span>, <span class="st">&quot;grey30&quot;</span>),</span>
<span id="cb20-24"><a href="#cb20-24" tabindex="-1"></a>              <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;Human-with-algorithm preferred&quot;</span>, <span class="st">&quot;Ambiguous&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span></span>
<span id="cb20-25"><a href="#cb20-25" tabindex="-1"></a>          ) <span class="sc">+</span></span>
<span id="cb20-26"><a href="#cb20-26" tabindex="-1"></a>  <span class="fu">scale_linetype_manual</span>(<span class="st">&quot;&quot;</span>, <span class="at">values =</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">1</span>, <span class="dv">3</span>), <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;Human-with-algorithm preferred&quot;</span>, <span class="st">&quot;Ambiguous&quot;</span>), <span class="at">drop =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="policy-learning" class="section level2">
<h2>Policy Learning</h2>
<p>Finally, we can conduct policy learning using the following codes.
The optimization requires <code>gurobi</code> software, and you may need
to install it separately (<a href="https://docs.gurobi.com/">documentation</a>). In this vignette, we
provide the codes for replicating the results in the paper (Figure 4 and
Table S1).</p>
<div id="helper-functions" class="section level3">
<h3>Helper functions</h3>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a><span class="fu">library</span>(gurobi)</span>
<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a><span class="do">## Optimization functions</span></span>
<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a>make_edge_mat <span class="ot">&lt;-</span> <span class="cf">function</span>(X) {</span>
<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a>  n <span class="ot">&lt;-</span> <span class="fu">nrow</span>(X)</span>
<span id="cb21-5"><a href="#cb21-5" tabindex="-1"></a>  <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span>n,</span>
<span id="cb21-6"><a href="#cb21-6" tabindex="-1"></a>         <span class="cf">function</span>(i) {</span>
<span id="cb21-7"><a href="#cb21-7" tabindex="-1"></a>           <span class="fu">vapply</span>(<span class="dv">1</span><span class="sc">:</span>n,</span>
<span id="cb21-8"><a href="#cb21-8" tabindex="-1"></a>                  <span class="cf">function</span>(j) {</span>
<span id="cb21-9"><a href="#cb21-9" tabindex="-1"></a>                    <span class="cf">if</span>(<span class="fu">all</span>(X[i,] <span class="sc">&lt;=</span> X[j,]) <span class="sc">&amp;</span> i <span class="sc">!=</span> j) {</span>
<span id="cb21-10"><a href="#cb21-10" tabindex="-1"></a>                      e <span class="ot">&lt;-</span> <span class="fu">numeric</span>(<span class="dv">2</span> <span class="sc">*</span> n)</span>
<span id="cb21-11"><a href="#cb21-11" tabindex="-1"></a>                      e[j] <span class="ot">&lt;-</span> <span class="dv">1</span></span>
<span id="cb21-12"><a href="#cb21-12" tabindex="-1"></a>                      e[n <span class="sc">+</span> i] <span class="ot">&lt;-</span> <span class="sc">-</span><span class="dv">1</span></span>
<span id="cb21-13"><a href="#cb21-13" tabindex="-1"></a>                    } <span class="cf">else</span> {</span>
<span id="cb21-14"><a href="#cb21-14" tabindex="-1"></a>                      e <span class="ot">&lt;-</span> <span class="fu">numeric</span>(<span class="dv">2</span> <span class="sc">*</span> n)</span>
<span id="cb21-15"><a href="#cb21-15" tabindex="-1"></a>                    }</span>
<span id="cb21-16"><a href="#cb21-16" tabindex="-1"></a>                    <span class="fu">return</span>(e)</span>
<span id="cb21-17"><a href="#cb21-17" tabindex="-1"></a>                  },</span>
<span id="cb21-18"><a href="#cb21-18" tabindex="-1"></a>                  <span class="fu">numeric</span>(<span class="dv">2</span> <span class="sc">*</span> n)) <span class="sc">%&gt;%</span> <span class="fu">t</span>()</span>
<span id="cb21-19"><a href="#cb21-19" tabindex="-1"></a>         }) <span class="sc">%&gt;%</span> <span class="fu">do.call</span>(rbind, .) <span class="sc">%&gt;%</span> <span class="fu">unique</span>() <span class="sc">%&gt;%</span> Matrix<span class="sc">::</span><span class="fu">Matrix</span>()</span>
<span id="cb21-20"><a href="#cb21-20" tabindex="-1"></a>}</span>
<span id="cb21-21"><a href="#cb21-21" tabindex="-1"></a></span>
<span id="cb21-22"><a href="#cb21-22" tabindex="-1"></a>make_action_mat <span class="ot">&lt;-</span> <span class="cf">function</span>(n, k) {</span>
<span id="cb21-23"><a href="#cb21-23" tabindex="-1"></a>  <span class="fu">do.call</span>(cbind, <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span>k, <span class="cf">function</span>(a) a <span class="sc">*</span> Matrix<span class="sc">::</span><span class="fu">Diagonal</span>(n)))</span>
<span id="cb21-24"><a href="#cb21-24" tabindex="-1"></a>}</span>
<span id="cb21-25"><a href="#cb21-25" tabindex="-1"></a></span>
<span id="cb21-26"><a href="#cb21-26" tabindex="-1"></a>create_monotone_constraints <span class="ot">&lt;-</span> <span class="cf">function</span>(X, <span class="at">rev =</span> <span class="cn">FALSE</span>) {</span>
<span id="cb21-27"><a href="#cb21-27" tabindex="-1"></a>  n <span class="ot">&lt;-</span> <span class="fu">nrow</span>(X)</span>
<span id="cb21-28"><a href="#cb21-28" tabindex="-1"></a>  d <span class="ot">&lt;-</span> <span class="fu">ncol</span>(X)</span>
<span id="cb21-29"><a href="#cb21-29" tabindex="-1"></a>  </span>
<span id="cb21-30"><a href="#cb21-30" tabindex="-1"></a>  <span class="co"># policy vector is (pi(1,.),...,pi(A,.))</span></span>
<span id="cb21-31"><a href="#cb21-31" tabindex="-1"></a>  <span class="co"># sum to one constraints</span></span>
<span id="cb21-32"><a href="#cb21-32" tabindex="-1"></a>  pi_sum_to_one <span class="ot">&lt;-</span> <span class="fu">do.call</span>(cbind, <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">2</span>, <span class="cf">function</span>(x) Matrix<span class="sc">::</span><span class="fu">Diagonal</span>(n)))</span>
<span id="cb21-33"><a href="#cb21-33" tabindex="-1"></a>  rhs_sum_to_one <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="dv">1</span>, n)</span>
<span id="cb21-34"><a href="#cb21-34" tabindex="-1"></a>  sense_sum_to_one <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="st">&quot;=&quot;</span>, n)</span>
<span id="cb21-35"><a href="#cb21-35" tabindex="-1"></a>  </span>
<span id="cb21-36"><a href="#cb21-36" tabindex="-1"></a>  <span class="co"># monotonicy constraints</span></span>
<span id="cb21-37"><a href="#cb21-37" tabindex="-1"></a>  edge_mat <span class="ot">&lt;-</span> <span class="fu">make_edge_mat</span>(X)</span>
<span id="cb21-38"><a href="#cb21-38" tabindex="-1"></a>  action_mat <span class="ot">&lt;-</span> <span class="fu">make_action_mat</span>(n, <span class="dv">2</span>)</span>
<span id="cb21-39"><a href="#cb21-39" tabindex="-1"></a>  <span class="co"># the following codes insure that (1) edge_mat: select pairs that monotonicity should hold in a correct order (i.e. if X[i,]&lt;X[j,] then assign 1 to j and -1 to i) (2) rbind(action_mat, action_mat): if multiplied by a vector of binary actions (length = 2*n since there exist two actions), it selects the action that has been chosen (e.g. if action 1 = 0 and action 2 = 1 for a given observation, it gives the value of 2)</span></span>
<span id="cb21-40"><a href="#cb21-40" tabindex="-1"></a>  monotone_mat <span class="ot">&lt;-</span> edge_mat <span class="sc">%*%</span> <span class="fu">rbind</span>(action_mat, action_mat)</span>
<span id="cb21-41"><a href="#cb21-41" tabindex="-1"></a>  rhs_monotone <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="dv">0</span>, <span class="fu">nrow</span>(monotone_mat))</span>
<span id="cb21-42"><a href="#cb21-42" tabindex="-1"></a>  <span class="cf">if</span>(<span class="fu">isTRUE</span>(rev)) {</span>
<span id="cb21-43"><a href="#cb21-43" tabindex="-1"></a>    sense_monotone <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="st">&quot;&lt;=&quot;</span>, <span class="fu">nrow</span>(monotone_mat))</span>
<span id="cb21-44"><a href="#cb21-44" tabindex="-1"></a>  } <span class="cf">else</span> {</span>
<span id="cb21-45"><a href="#cb21-45" tabindex="-1"></a>    sense_monotone <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="st">&quot;&gt;=&quot;</span>, <span class="fu">nrow</span>(monotone_mat))</span>
<span id="cb21-46"><a href="#cb21-46" tabindex="-1"></a>  }</span>
<span id="cb21-47"><a href="#cb21-47" tabindex="-1"></a>  A <span class="ot">&lt;-</span> <span class="fu">rbind</span>(pi_sum_to_one, monotone_mat)</span>
<span id="cb21-48"><a href="#cb21-48" tabindex="-1"></a>  rhs <span class="ot">&lt;-</span> <span class="fu">c</span>(rhs_sum_to_one, rhs_monotone)</span>
<span id="cb21-49"><a href="#cb21-49" tabindex="-1"></a>  sense <span class="ot">&lt;-</span> <span class="fu">c</span>(sense_sum_to_one, sense_monotone)</span>
<span id="cb21-50"><a href="#cb21-50" tabindex="-1"></a>  </span>
<span id="cb21-51"><a href="#cb21-51" tabindex="-1"></a>  <span class="co"># restrict variables</span></span>
<span id="cb21-52"><a href="#cb21-52" tabindex="-1"></a>  vtype <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="st">&quot;B&quot;</span>, n <span class="sc">*</span> <span class="dv">2</span>) <span class="co"># binary policy decisions</span></span>
<span id="cb21-53"><a href="#cb21-53" tabindex="-1"></a>  </span>
<span id="cb21-54"><a href="#cb21-54" tabindex="-1"></a>  <span class="fu">return</span>(<span class="fu">list</span>(<span class="at">A =</span> A, <span class="at">rhs =</span> rhs, <span class="at">sense =</span> sense, <span class="at">vtype =</span> vtype))</span>
<span id="cb21-55"><a href="#cb21-55" tabindex="-1"></a>}</span>
<span id="cb21-56"><a href="#cb21-56" tabindex="-1"></a></span>
<span id="cb21-57"><a href="#cb21-57" tabindex="-1"></a>get_monotone_policy <span class="ot">&lt;-</span> <span class="cf">function</span>(X, wts, <span class="at">rev =</span> <span class="cn">FALSE</span>) {</span>
<span id="cb21-58"><a href="#cb21-58" tabindex="-1"></a>  n <span class="ot">&lt;-</span> <span class="fu">nrow</span>(X)</span>
<span id="cb21-59"><a href="#cb21-59" tabindex="-1"></a>  <span class="co"># get the constraints</span></span>
<span id="cb21-60"><a href="#cb21-60" tabindex="-1"></a>  model <span class="ot">&lt;-</span> <span class="fu">create_monotone_constraints</span>(<span class="fu">as.matrix</span>(X), <span class="at">rev =</span> rev)</span>
<span id="cb21-61"><a href="#cb21-61" tabindex="-1"></a>  <span class="co"># solve</span></span>
<span id="cb21-62"><a href="#cb21-62" tabindex="-1"></a>  model<span class="sc">$</span>obj <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">numeric</span>(n), wts)</span>
<span id="cb21-63"><a href="#cb21-63" tabindex="-1"></a>  model<span class="sc">$</span>modelsense <span class="ot">&lt;-</span> <span class="st">&quot;min&quot;</span></span>
<span id="cb21-64"><a href="#cb21-64" tabindex="-1"></a>  sol <span class="ot">&lt;-</span> <span class="fu">gurobi</span>(model)</span>
<span id="cb21-65"><a href="#cb21-65" tabindex="-1"></a>  </span>
<span id="cb21-66"><a href="#cb21-66" tabindex="-1"></a>  <span class="co"># extract values</span></span>
<span id="cb21-67"><a href="#cb21-67" tabindex="-1"></a>  policy <span class="ot">&lt;-</span> <span class="fu">apply</span>(<span class="fu">matrix</span>(sol<span class="sc">$</span>x, <span class="at">ncol =</span> <span class="dv">2</span>), <span class="dv">1</span>, which.max) <span class="sc">-</span> <span class="dv">1</span></span>
<span id="cb21-68"><a href="#cb21-68" tabindex="-1"></a>  </span>
<span id="cb21-69"><a href="#cb21-69" tabindex="-1"></a>  <span class="fu">return</span>(policy)</span>
<span id="cb21-70"><a href="#cb21-70" tabindex="-1"></a>}</span>
<span id="cb21-71"><a href="#cb21-71" tabindex="-1"></a></span>
<span id="cb21-72"><a href="#cb21-72" tabindex="-1"></a>get_monotone_policy_ai <span class="ot">&lt;-</span> <span class="cf">function</span>(X, wts, <span class="at">rev =</span> <span class="cn">FALSE</span>) {</span>
<span id="cb21-73"><a href="#cb21-73" tabindex="-1"></a>  X_df <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(X)</span>
<span id="cb21-74"><a href="#cb21-74" tabindex="-1"></a>  wts_df <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(wts)</span>
<span id="cb21-75"><a href="#cb21-75" tabindex="-1"></a>  <span class="fu">colnames</span>(wts_df) <span class="ot">&lt;-</span> <span class="st">&quot;wts&quot;</span></span>
<span id="cb21-76"><a href="#cb21-76" tabindex="-1"></a>  comb_df <span class="ot">&lt;-</span> <span class="fu">bind_cols</span>(X_df, wts_df)</span>
<span id="cb21-77"><a href="#cb21-77" tabindex="-1"></a>  </span>
<span id="cb21-78"><a href="#cb21-78" tabindex="-1"></a>  <span class="co"># get distinct X values and the sum of the weights on them</span></span>
<span id="cb21-79"><a href="#cb21-79" tabindex="-1"></a>  grouped_df <span class="ot">&lt;-</span> comb_df <span class="sc">%&gt;%</span></span>
<span id="cb21-80"><a href="#cb21-80" tabindex="-1"></a>    <span class="fu">group_by</span>(<span class="fu">across</span>(<span class="sc">-</span>wts)) <span class="sc">%&gt;%</span></span>
<span id="cb21-81"><a href="#cb21-81" tabindex="-1"></a>    <span class="fu">summarise</span>(<span class="fu">across</span>(<span class="fu">everything</span>(), sum),</span>
<span id="cb21-82"><a href="#cb21-82" tabindex="-1"></a>              <span class="at">n =</span> <span class="fu">n</span>(),</span>
<span id="cb21-83"><a href="#cb21-83" tabindex="-1"></a>              <span class="at">.groups =</span> <span class="st">&quot;drop&quot;</span></span>
<span id="cb21-84"><a href="#cb21-84" tabindex="-1"></a>    )</span>
<span id="cb21-85"><a href="#cb21-85" tabindex="-1"></a>  </span>
<span id="cb21-86"><a href="#cb21-86" tabindex="-1"></a>  X_distinct <span class="ot">&lt;-</span> grouped_df <span class="sc">%&gt;%</span></span>
<span id="cb21-87"><a href="#cb21-87" tabindex="-1"></a>    <span class="fu">select</span>(<span class="sc">-</span>wts, <span class="sc">-</span>n) <span class="sc">%&gt;%</span></span>
<span id="cb21-88"><a href="#cb21-88" tabindex="-1"></a>    <span class="fu">as.matrix</span>()</span>
<span id="cb21-89"><a href="#cb21-89" tabindex="-1"></a>  wts_distinct <span class="ot">&lt;-</span> grouped_df <span class="sc">%&gt;%</span> <span class="fu">pull</span>(wts)</span>
<span id="cb21-90"><a href="#cb21-90" tabindex="-1"></a>  </span>
<span id="cb21-91"><a href="#cb21-91" tabindex="-1"></a>  policy <span class="ot">&lt;-</span> <span class="fu">get_monotone_policy</span>(X_distinct, wts_distinct, <span class="at">rev =</span> rev)</span>
<span id="cb21-92"><a href="#cb21-92" tabindex="-1"></a>  </span>
<span id="cb21-93"><a href="#cb21-93" tabindex="-1"></a>  grouped_df <span class="ot">&lt;-</span> grouped_df <span class="sc">%&gt;%</span></span>
<span id="cb21-94"><a href="#cb21-94" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">policy =</span> policy)</span>
<span id="cb21-95"><a href="#cb21-95" tabindex="-1"></a>  </span>
<span id="cb21-96"><a href="#cb21-96" tabindex="-1"></a>  <span class="fu">return</span>(grouped_df)</span>
<span id="cb21-97"><a href="#cb21-97" tabindex="-1"></a>}</span>
<span id="cb21-98"><a href="#cb21-98" tabindex="-1"></a></span>
<span id="cb21-99"><a href="#cb21-99" tabindex="-1"></a><span class="do">## Weights functions</span></span>
<span id="cb21-100"><a href="#cb21-100" tabindex="-1"></a>compute_wts_dr <span class="ot">&lt;-</span> <span class="cf">function</span>(Y, D, Z, nuis_funcs, <span class="at">pscore =</span> <span class="cn">NULL</span>, <span class="at">l01 =</span> <span class="dv">1</span>) {</span>
<span id="cb21-101"><a href="#cb21-101" tabindex="-1"></a>  <span class="do">## update the propensity score if provided</span></span>
<span id="cb21-102"><a href="#cb21-102" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(pscore)) {</span>
<span id="cb21-103"><a href="#cb21-103" tabindex="-1"></a>    nuis_funcs<span class="sc">$</span>pscore <span class="ot">&lt;-</span> pscore</span>
<span id="cb21-104"><a href="#cb21-104" tabindex="-1"></a>  }</span>
<span id="cb21-105"><a href="#cb21-105" tabindex="-1"></a>  </span>
<span id="cb21-106"><a href="#cb21-106" tabindex="-1"></a>  <span class="do">## check whether Z is 0/1 binary variable</span></span>
<span id="cb21-107"><a href="#cb21-107" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">any</span>(<span class="fu">sort</span>(<span class="fu">unique</span>(Z)) <span class="sc">!=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>))) {</span>
<span id="cb21-108"><a href="#cb21-108" tabindex="-1"></a>    <span class="fu">stop</span>(<span class="st">&quot;Z must be a binary variable&quot;</span>)</span>
<span id="cb21-109"><a href="#cb21-109" tabindex="-1"></a>  }</span>
<span id="cb21-110"><a href="#cb21-110" tabindex="-1"></a>  dat <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">Y =</span> Y, <span class="at">D =</span> D, <span class="at">Z =</span> Z, <span class="at">pscore =</span> nuis_funcs<span class="sc">$</span>pscore)</span>
<span id="cb21-111"><a href="#cb21-111" tabindex="-1"></a>  </span>
<span id="cb21-112"><a href="#cb21-112" tabindex="-1"></a>  preds <span class="ot">&lt;-</span> nuis_funcs<span class="sc">$</span>z_models <span class="sc">|&gt;</span></span>
<span id="cb21-113"><a href="#cb21-113" tabindex="-1"></a>    <span class="fu">pivot_wider</span>(<span class="at">names_from =</span> <span class="fu">c</span>(Z), <span class="at">values_from =</span> <span class="fu">c</span>(<span class="sc">-</span>Z, <span class="sc">-</span>idx), <span class="at">names_sep =</span> <span class="st">&quot;&quot;</span>) <span class="sc">|&gt;</span></span>
<span id="cb21-114"><a href="#cb21-114" tabindex="-1"></a>    <span class="fu">select</span>(<span class="sc">-</span>idx)</span>
<span id="cb21-115"><a href="#cb21-115" tabindex="-1"></a>  </span>
<span id="cb21-116"><a href="#cb21-116" tabindex="-1"></a>  dat <span class="ot">&lt;-</span> dat <span class="sc">%&gt;%</span></span>
<span id="cb21-117"><a href="#cb21-117" tabindex="-1"></a>    <span class="fu">bind_cols</span>(preds)</span>
<span id="cb21-118"><a href="#cb21-118" tabindex="-1"></a>  </span>
<span id="cb21-119"><a href="#cb21-119" tabindex="-1"></a>  wts <span class="ot">&lt;-</span> dat <span class="sc">%&gt;%</span></span>
<span id="cb21-120"><a href="#cb21-120" tabindex="-1"></a>    <span class="fu">mutate</span>(</span>
<span id="cb21-121"><a href="#cb21-121" tabindex="-1"></a>      <span class="at">p.1i =</span> (<span class="dv">1</span> <span class="sc">-</span> d_pred1) <span class="sc">*</span> ((<span class="dv">1</span> <span class="sc">+</span> l01) <span class="sc">*</span> y_pred1 <span class="sc">-</span> l01) <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">+</span> l01) <span class="sc">*</span> Z <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> D) <span class="sc">*</span> (Y <span class="sc">-</span> y_pred1) <span class="sc">/</span> pscore <span class="sc">-</span> ((<span class="dv">1</span> <span class="sc">+</span> l01) <span class="sc">*</span> y_pred1 <span class="sc">-</span> l01) <span class="sc">*</span> Z <span class="sc">*</span> (D <span class="sc">-</span> d_pred1) <span class="sc">/</span> pscore,</span>
<span id="cb21-122"><a href="#cb21-122" tabindex="-1"></a>      <span class="at">p.0i =</span> (<span class="dv">1</span> <span class="sc">-</span> d_pred0) <span class="sc">*</span> ((<span class="dv">1</span> <span class="sc">+</span> l01) <span class="sc">*</span> y_pred0 <span class="sc">-</span> l01) <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">+</span> l01) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> Z) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> D) <span class="sc">*</span> (Y <span class="sc">-</span> y_pred0) <span class="sc">/</span> (<span class="dv">1</span> <span class="sc">-</span> pscore) <span class="sc">-</span> ((<span class="dv">1</span> <span class="sc">+</span> l01) <span class="sc">*</span> y_pred0 <span class="sc">-</span> l01) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> Z) <span class="sc">*</span> (D <span class="sc">-</span> d_pred0) <span class="sc">/</span> (<span class="dv">1</span> <span class="sc">-</span> pscore),</span>
<span id="cb21-123"><a href="#cb21-123" tabindex="-1"></a>      <span class="at">p.i =</span> p<span class="fl">.1</span>i <span class="sc">-</span> p<span class="fl">.0</span>i</span>
<span id="cb21-124"><a href="#cb21-124" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb21-125"><a href="#cb21-125" tabindex="-1"></a>    <span class="fu">pull</span>(p.i)</span>
<span id="cb21-126"><a href="#cb21-126" tabindex="-1"></a>  </span>
<span id="cb21-127"><a href="#cb21-127" tabindex="-1"></a>  <span class="fu">return</span>(wts)</span>
<span id="cb21-128"><a href="#cb21-128" tabindex="-1"></a>}</span>
<span id="cb21-129"><a href="#cb21-129" tabindex="-1"></a></span>
<span id="cb21-130"><a href="#cb21-130" tabindex="-1"></a>compute_bound_wts_dr <span class="ot">&lt;-</span> <span class="cf">function</span>(Y, A, D, Z, nuis_funcs1, nuis_funcs2, <span class="at">pscore =</span> <span class="cn">NULL</span>, <span class="at">l01 =</span> <span class="dv">1</span>) {</span>
<span id="cb21-131"><a href="#cb21-131" tabindex="-1"></a>  <span class="do">## update the propensity score if provided</span></span>
<span id="cb21-132"><a href="#cb21-132" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(pscore)) {</span>
<span id="cb21-133"><a href="#cb21-133" tabindex="-1"></a>    nuis_funcs1<span class="sc">$</span>pscore <span class="ot">&lt;-</span> pscore</span>
<span id="cb21-134"><a href="#cb21-134" tabindex="-1"></a>    nuis_funcs2<span class="sc">$</span>pscore <span class="ot">&lt;-</span> pscore</span>
<span id="cb21-135"><a href="#cb21-135" tabindex="-1"></a>  }</span>
<span id="cb21-136"><a href="#cb21-136" tabindex="-1"></a>  </span>
<span id="cb21-137"><a href="#cb21-137" tabindex="-1"></a>  <span class="do">## check whether Z is 0/1 binary variable</span></span>
<span id="cb21-138"><a href="#cb21-138" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">any</span>(<span class="fu">sort</span>(<span class="fu">unique</span>(Z)) <span class="sc">!=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>))) {</span>
<span id="cb21-139"><a href="#cb21-139" tabindex="-1"></a>    <span class="fu">stop</span>(<span class="st">&quot;Z must be a binary variable&quot;</span>)</span>
<span id="cb21-140"><a href="#cb21-140" tabindex="-1"></a>  }</span>
<span id="cb21-141"><a href="#cb21-141" tabindex="-1"></a>  data <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">Y =</span> Y, <span class="at">D =</span> D, <span class="at">Z =</span> Z, <span class="at">A =</span> A, <span class="at">pscore =</span> nuis_funcs1<span class="sc">$</span>pscore)</span>
<span id="cb21-142"><a href="#cb21-142" tabindex="-1"></a>  </span>
<span id="cb21-143"><a href="#cb21-143" tabindex="-1"></a>  preds1 <span class="ot">&lt;-</span> nuis_funcs1<span class="sc">$</span>z_models <span class="sc">|&gt;</span></span>
<span id="cb21-144"><a href="#cb21-144" tabindex="-1"></a>    <span class="fu">pivot_wider</span>(<span class="at">names_from =</span> <span class="fu">c</span>(Z), <span class="at">values_from =</span> <span class="fu">c</span>(<span class="sc">-</span>Z, <span class="sc">-</span>idx), <span class="at">names_sep =</span> <span class="st">&quot;&quot;</span>) <span class="sc">|&gt;</span></span>
<span id="cb21-145"><a href="#cb21-145" tabindex="-1"></a>    <span class="fu">select</span>(<span class="sc">-</span>idx)</span>
<span id="cb21-146"><a href="#cb21-146" tabindex="-1"></a>  </span>
<span id="cb21-147"><a href="#cb21-147" tabindex="-1"></a>  preds2 <span class="ot">&lt;-</span> nuis_funcs2<span class="sc">$</span>z_models <span class="sc">|&gt;</span></span>
<span id="cb21-148"><a href="#cb21-148" tabindex="-1"></a>    <span class="fu">pivot_wider</span>(<span class="at">names_from =</span> <span class="fu">c</span>(Z, A), <span class="at">values_from =</span> <span class="fu">c</span>(<span class="sc">-</span>Z, <span class="sc">-</span>A, <span class="sc">-</span>idx), <span class="at">names_sep =</span> <span class="st">&quot;&quot;</span>) <span class="sc">|&gt;</span></span>
<span id="cb21-149"><a href="#cb21-149" tabindex="-1"></a>    <span class="fu">select</span>(<span class="sc">-</span>idx)</span>
<span id="cb21-150"><a href="#cb21-150" tabindex="-1"></a>  </span>
<span id="cb21-151"><a href="#cb21-151" tabindex="-1"></a>  data <span class="ot">&lt;-</span> data <span class="sc">%&gt;%</span></span>
<span id="cb21-152"><a href="#cb21-152" tabindex="-1"></a>    <span class="fu">bind_cols</span>(preds1, preds2)</span>
<span id="cb21-153"><a href="#cb21-153" tabindex="-1"></a>  </span>
<span id="cb21-154"><a href="#cb21-154" tabindex="-1"></a>  wts <span class="ot">&lt;-</span> data <span class="sc">%&gt;%</span></span>
<span id="cb21-155"><a href="#cb21-155" tabindex="-1"></a>    <span class="fu">mutate</span>(</span>
<span id="cb21-156"><a href="#cb21-156" tabindex="-1"></a>      <span class="co"># gU0.i = 1 if z&#39;=1 i.e. Pr(Y=0,D=0|A=0,Z=1,X=x) &gt; Pr(Y=0,D=0|A=0,Z=0,X=x)</span></span>
<span id="cb21-157"><a href="#cb21-157" tabindex="-1"></a>      <span class="at">gU0.i =</span> <span class="dv">1</span> <span class="sc">*</span> ((<span class="dv">1</span> <span class="sc">-</span> d_pred10) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> y_pred10) <span class="sc">&gt;=</span> (<span class="dv">1</span> <span class="sc">-</span> d_pred00) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> y_pred00)),</span>
<span id="cb21-158"><a href="#cb21-158" tabindex="-1"></a>      <span class="co"># phi_z0(x): Pr(Y=0,D=0,A=0|Z=z,X=x)</span></span>
<span id="cb21-159"><a href="#cb21-159" tabindex="-1"></a>      <span class="at">p10.i =</span> (<span class="dv">1</span> <span class="sc">-</span> A) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> d_pred10) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> y_pred10) <span class="sc">-</span> Z <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> A) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> D) <span class="sc">*</span> (Y <span class="sc">-</span> y_pred10) <span class="sc">/</span> pscore <span class="sc">-</span> (<span class="dv">1</span> <span class="sc">-</span> y_pred10) <span class="sc">*</span> Z <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> A) <span class="sc">*</span> (D <span class="sc">-</span> d_pred10) <span class="sc">/</span> pscore,</span>
<span id="cb21-160"><a href="#cb21-160" tabindex="-1"></a>      <span class="at">p00.i =</span> (<span class="dv">1</span> <span class="sc">-</span> A) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> d_pred00) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> y_pred00) <span class="sc">-</span> (<span class="dv">1</span> <span class="sc">-</span> Z) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> A) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> D) <span class="sc">*</span> (Y <span class="sc">-</span> y_pred00) <span class="sc">/</span> (<span class="dv">1</span> <span class="sc">-</span> pscore) <span class="sc">-</span> (<span class="dv">1</span> <span class="sc">-</span> y_pred00) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> Z) <span class="sc">*</span> (<span class="dv">1</span> <span class="sc">-</span> A) <span class="sc">*</span> (D <span class="sc">-</span> d_pred00) <span class="sc">/</span> (<span class="dv">1</span> <span class="sc">-</span> pscore)</span>
<span id="cb21-161"><a href="#cb21-161" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb21-162"><a href="#cb21-162" tabindex="-1"></a>    <span class="fu">mutate</span>(</span>
<span id="cb21-163"><a href="#cb21-163" tabindex="-1"></a>      <span class="at">p10_gU0.i =</span> p10.i <span class="sc">*</span> gU0.i,</span>
<span id="cb21-164"><a href="#cb21-164" tabindex="-1"></a>      <span class="at">p00_gU0.i =</span> p00.i <span class="sc">*</span> gU0.i</span>
<span id="cb21-165"><a href="#cb21-165" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb21-166"><a href="#cb21-166" tabindex="-1"></a>    <span class="fu">mutate</span>(</span>
<span id="cb21-167"><a href="#cb21-167" tabindex="-1"></a>      <span class="at">fn_diff_ub.0i =</span> p01.i <span class="sc">-</span> p0.i <span class="sc">+</span> p00.D.i <span class="sc">-</span> p10_gU0.i <span class="sc">+</span> p00_gU0.i,</span>
<span id="cb21-168"><a href="#cb21-168" tabindex="-1"></a>      <span class="at">fp_diff_ub.0i =</span> p01.i <span class="sc">-</span> p0.i <span class="sc">+</span> p01.D.i <span class="sc">-</span> p10_gU0.i <span class="sc">+</span> p00_gU0.i,</span>
<span id="cb21-169"><a href="#cb21-169" tabindex="-1"></a>      <span class="at">loss_diff_ub.0i =</span> fn_diff_ub<span class="fl">.0</span>i <span class="sc">+</span> l01 <span class="sc">*</span> fp_diff_ub<span class="fl">.0</span>i</span>
<span id="cb21-170"><a href="#cb21-170" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb21-171"><a href="#cb21-171" tabindex="-1"></a>    <span class="fu">pull</span>(loss_diff_ub<span class="fl">.0</span>i)</span>
<span id="cb21-172"><a href="#cb21-172" tabindex="-1"></a>  </span>
<span id="cb21-173"><a href="#cb21-173" tabindex="-1"></a>  <span class="fu">return</span>(wts)</span>
<span id="cb21-174"><a href="#cb21-174" tabindex="-1"></a>}</span></code></pre></div>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a>get_policy_value <span class="ot">&lt;-</span> <span class="cf">function</span>(policy, <span class="at">n_obs =</span> <span class="dv">1891</span>) {</span>
<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a>    v <span class="ot">&lt;-</span> policy <span class="sc">|&gt;</span></span>
<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a>        <span class="fu">mutate</span>(<span class="at">wts_policy =</span> wts <span class="sc">*</span> policy) <span class="sc">|&gt;</span></span>
<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a>        <span class="fu">pull</span>(wts_policy) <span class="sc">|&gt;</span></span>
<span id="cb22-5"><a href="#cb22-5" tabindex="-1"></a>        <span class="fu">sum</span>()</span>
<span id="cb22-6"><a href="#cb22-6" tabindex="-1"></a>    <span class="fu">return</span>(v<span class="sc">/</span>n_obs)</span>
<span id="cb22-7"><a href="#cb22-7" tabindex="-1"></a>}</span></code></pre></div>
</div>
<div id="whether-to-provide-psa" class="section level3">
<h3>Whether to provide PSA</h3>
<p>Let’s consider policy class with an increasing monotonicity
constraint.</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a>psaX <span class="ot">&lt;-</span> cov_mat[,<span class="fu">c</span>(<span class="st">&quot;FTAScore&quot;</span>, <span class="st">&quot;NCAScore&quot;</span>, <span class="st">&quot;NVCAFlag&quot;</span>)]</span>
<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a>nca_wts <span class="ot">&lt;-</span> <span class="fu">compute_wts_dr</span>(<span class="at">Y =</span> Y, <span class="at">D =</span> D, <span class="at">Z =</span> Z, <span class="at">nuis_funcs =</span> nuis_func, <span class="at">pscore =</span> <span class="fl">0.5</span>, <span class="at">l01 =</span> <span class="dv">1</span>)</span>
<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a>nca_provide_policy <span class="ot">&lt;-</span> <span class="fu">get_monotone_policy_ai</span>(<span class="at">X =</span> psaX, <span class="at">wts =</span> nca_wts)</span></code></pre></div>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="do">## The estimated values of the empirical risk minimization problem</span></span>
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a><span class="fu">get_policy_value</span>(nca_provide_policy, <span class="at">n_obs =</span> <span class="fu">length</span>(Y))</span>
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a><span class="co">#&gt; [1] -0.01014338</span></span>
<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a></span>
<span id="cb24-5"><a href="#cb24-5" tabindex="-1"></a><span class="do">## Visualization</span></span>
<span id="cb24-6"><a href="#cb24-6" tabindex="-1"></a>nca_provide_policy <span class="sc">|&gt;</span></span>
<span id="cb24-7"><a href="#cb24-7" tabindex="-1"></a>    <span class="fu">filter</span>(NVCAFlag <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span></span>
<span id="cb24-8"><a href="#cb24-8" tabindex="-1"></a>    <span class="fu">mutate</span>(</span>
<span id="cb24-9"><a href="#cb24-9" tabindex="-1"></a>            <span class="at">policy =</span> <span class="fu">factor</span>(policy, <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)),</span>
<span id="cb24-10"><a href="#cb24-10" tabindex="-1"></a>            <span class="at">NVCAFlag =</span> <span class="fu">paste0</span>(<span class="st">&quot;NVCA Flag: &quot;</span>, NVCAFlag)</span>
<span id="cb24-11"><a href="#cb24-11" tabindex="-1"></a>        ) <span class="sc">|&gt;</span></span>
<span id="cb24-12"><a href="#cb24-12" tabindex="-1"></a>        <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> NCAScore, <span class="at">y =</span> FTAScore, <span class="at">fill =</span> policy)) <span class="sc">+</span></span>
<span id="cb24-13"><a href="#cb24-13" tabindex="-1"></a>        <span class="fu">geom_tile</span>(<span class="at">color =</span> <span class="st">&quot;grey50&quot;</span>) <span class="sc">+</span></span>
<span id="cb24-14"><a href="#cb24-14" tabindex="-1"></a>        <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fl">2.5</span>, <span class="at">xend =</span> <span class="fl">4.5</span>, <span class="at">y =</span> <span class="fl">4.5</span>, <span class="at">yend =</span> <span class="fl">4.5</span>), <span class="at">lty =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb24-15"><a href="#cb24-15" tabindex="-1"></a>        <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fl">4.5</span>, <span class="at">xend =</span> <span class="fl">4.5</span>, <span class="at">y =</span> <span class="fl">4.5</span>, <span class="at">yend =</span> <span class="fl">1.5</span>), <span class="at">lty =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb24-16"><a href="#cb24-16" tabindex="-1"></a>        <span class="fu">facet_grid</span>(. <span class="sc">~</span> NVCAFlag) <span class="sc">+</span></span>
<span id="cb24-17"><a href="#cb24-17" tabindex="-1"></a>        <span class="fu">scale_fill_brewer</span>(<span class="st">&quot;PSA Recommendation Provision&quot;</span>,</span>
<span id="cb24-18"><a href="#cb24-18" tabindex="-1"></a>            <span class="at">type =</span> <span class="st">&quot;seq&quot;</span>, <span class="at">palette =</span> <span class="st">&quot;Blues&quot;</span>,</span>
<span id="cb24-19"><a href="#cb24-19" tabindex="-1"></a>            <span class="at">direction =</span> <span class="dv">1</span></span>
<span id="cb24-20"><a href="#cb24-20" tabindex="-1"></a>        ) <span class="sc">+</span></span>
<span id="cb24-21"><a href="#cb24-21" tabindex="-1"></a>        <span class="fu">scale_y_reverse</span>(<span class="at">breaks =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">6</span>) <span class="sc">+</span></span>
<span id="cb24-22"><a href="#cb24-22" tabindex="-1"></a>        <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">6</span>) <span class="sc">+</span></span>
<span id="cb24-23"><a href="#cb24-23" tabindex="-1"></a>        <span class="fu">expand_limits</span>(<span class="at">x =</span> <span class="fl">0.5</span><span class="sc">:</span><span class="dv">6</span>, <span class="at">y =</span><span class="fl">0.5</span><span class="sc">:</span><span class="dv">6</span>) <span class="sc">+</span></span>
<span id="cb24-24"><a href="#cb24-24" tabindex="-1"></a>        <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;NCA Score&quot;</span>, </span>
<span id="cb24-25"><a href="#cb24-25" tabindex="-1"></a>             <span class="at">y =</span> <span class="st">&quot;FTA Score&quot;</span>,</span>
<span id="cb24-26"><a href="#cb24-26" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Whether to provide PSA recommendations&quot;</span>) <span class="sc">+</span></span>
<span id="cb24-27"><a href="#cb24-27" tabindex="-1"></a>        <span class="fu">coord_fixed</span>(<span class="at">expand =</span> F) <span class="sc">+</span></span>
<span id="cb24-28"><a href="#cb24-28" tabindex="-1"></a>        <span class="fu">theme_classic</span>(<span class="at">base_size =</span> <span class="dv">15</span>) <span class="sc">+</span></span>
<span id="cb24-29"><a href="#cb24-29" tabindex="-1"></a>        <span class="fu">scale_fill_brewer</span>(<span class="st">&quot;&quot;</span>, <span class="at">breaks =</span> <span class="dv">0</span><span class="sc">:</span><span class="dv">1</span>, <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Not Provide&quot;</span>, <span class="st">&quot;Provide&quot;</span>)) <span class="sc">+</span></span>
<span id="cb24-30"><a href="#cb24-30" tabindex="-1"></a>        <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>, <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>)) <span class="sc">+</span></span>
<span id="cb24-31"><a href="#cb24-31" tabindex="-1"></a>        <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> n), <span class="at">color =</span> <span class="st">&quot;grey10&quot;</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>We can also consider policy class with a decreasing monotonicity
constraint.</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a>nca_provide_policy_dec <span class="ot">&lt;-</span> <span class="fu">get_monotone_policy_ai</span>(<span class="at">X =</span> psaX, <span class="at">wts =</span> nca_wts, <span class="at">rev =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a><span class="fu">get_policy_value</span>(nca_provide_policy_dec, <span class="at">n_obs =</span> <span class="fu">length</span>(Y))</span>
<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a><span class="co">#&gt; [1] -0.008533944</span></span></code></pre></div>
</div>
<div id="whether-to-follow-psa" class="section level3">
<h3>Whether to follow PSA</h3>
<p>Let’s consider policy class with an increasing monotonicity
constraint.</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>nca_wts <span class="ot">&lt;-</span> <span class="fu">compute_bound_wts_dr</span>(<span class="at">Y =</span> Y, <span class="at">A =</span> A, <span class="at">D =</span> D, <span class="at">Z =</span> Z, <span class="at">nuis_funcs1 =</span> nuis_func, <span class="at">nuis_funcs2 =</span> nuis_func_ai, <span class="at">pscore =</span> <span class="fl">0.5</span>, <span class="at">l01 =</span> <span class="dv">1</span>)</span>
<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>nca_follow_policy <span class="ot">&lt;-</span> <span class="fu">get_monotone_policy_ai</span>(<span class="at">X =</span> psaX, <span class="at">wts =</span> nca_wts)</span></code></pre></div>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="do">## The estimated values of the empirical risk minimization problem</span></span>
<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a><span class="fu">get_policy_value</span>(nca_follow_policy, <span class="at">n_obs =</span> <span class="fu">length</span>(Y))</span>
<span id="cb28-3"><a href="#cb28-3" tabindex="-1"></a><span class="co">#&gt; [1] -0.003514698</span></span>
<span id="cb28-4"><a href="#cb28-4" tabindex="-1"></a></span>
<span id="cb28-5"><a href="#cb28-5" tabindex="-1"></a><span class="do">## Visualization</span></span>
<span id="cb28-6"><a href="#cb28-6" tabindex="-1"></a>nca_follow_policy <span class="sc">|&gt;</span></span>
<span id="cb28-7"><a href="#cb28-7" tabindex="-1"></a>    <span class="fu">filter</span>(NVCAFlag <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span></span>
<span id="cb28-8"><a href="#cb28-8" tabindex="-1"></a>    <span class="fu">mutate</span>(</span>
<span id="cb28-9"><a href="#cb28-9" tabindex="-1"></a>            <span class="at">policy =</span> <span class="fu">factor</span>(policy, <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)),</span>
<span id="cb28-10"><a href="#cb28-10" tabindex="-1"></a>            <span class="at">NVCAFlag =</span> <span class="fu">paste0</span>(<span class="st">&quot;NVCA Flag: &quot;</span>, NVCAFlag)</span>
<span id="cb28-11"><a href="#cb28-11" tabindex="-1"></a>        ) <span class="sc">|&gt;</span></span>
<span id="cb28-12"><a href="#cb28-12" tabindex="-1"></a>        <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> NCAScore, <span class="at">y =</span> FTAScore, <span class="at">fill =</span> policy)) <span class="sc">+</span></span>
<span id="cb28-13"><a href="#cb28-13" tabindex="-1"></a>        <span class="fu">geom_tile</span>(<span class="at">color =</span> <span class="st">&quot;grey50&quot;</span>) <span class="sc">+</span></span>
<span id="cb28-14"><a href="#cb28-14" tabindex="-1"></a>        <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fl">2.5</span>, <span class="at">xend =</span> <span class="fl">4.5</span>, <span class="at">y =</span> <span class="fl">4.5</span>, <span class="at">yend =</span> <span class="fl">4.5</span>), <span class="at">lty =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb28-15"><a href="#cb28-15" tabindex="-1"></a>        <span class="fu">geom_segment</span>(<span class="fu">aes</span>(<span class="at">x =</span> <span class="fl">4.5</span>, <span class="at">xend =</span> <span class="fl">4.5</span>, <span class="at">y =</span> <span class="fl">4.5</span>, <span class="at">yend =</span> <span class="fl">1.5</span>), <span class="at">lty =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb28-16"><a href="#cb28-16" tabindex="-1"></a>        <span class="fu">facet_grid</span>(. <span class="sc">~</span> NVCAFlag) <span class="sc">+</span></span>
<span id="cb28-17"><a href="#cb28-17" tabindex="-1"></a>        <span class="fu">scale_fill_brewer</span>(<span class="st">&quot;PSA Recommendation Provision&quot;</span>,</span>
<span id="cb28-18"><a href="#cb28-18" tabindex="-1"></a>            <span class="at">type =</span> <span class="st">&quot;seq&quot;</span>, <span class="at">palette =</span> <span class="st">&quot;Blues&quot;</span>,</span>
<span id="cb28-19"><a href="#cb28-19" tabindex="-1"></a>            <span class="at">direction =</span> <span class="dv">1</span></span>
<span id="cb28-20"><a href="#cb28-20" tabindex="-1"></a>        ) <span class="sc">+</span></span>
<span id="cb28-21"><a href="#cb28-21" tabindex="-1"></a>        <span class="fu">scale_y_reverse</span>(<span class="at">breaks =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">6</span>) <span class="sc">+</span></span>
<span id="cb28-22"><a href="#cb28-22" tabindex="-1"></a>        <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">6</span>) <span class="sc">+</span></span>
<span id="cb28-23"><a href="#cb28-23" tabindex="-1"></a>        <span class="fu">expand_limits</span>(<span class="at">x =</span> <span class="fl">0.5</span><span class="sc">:</span><span class="dv">6</span>, <span class="at">y =</span><span class="fl">0.5</span><span class="sc">:</span><span class="dv">6</span>) <span class="sc">+</span></span>
<span id="cb28-24"><a href="#cb28-24" tabindex="-1"></a>        <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;NCA Score&quot;</span>, </span>
<span id="cb28-25"><a href="#cb28-25" tabindex="-1"></a>             <span class="at">y =</span> <span class="st">&quot;FTA Score&quot;</span>,</span>
<span id="cb28-26"><a href="#cb28-26" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Whether to follow PSA recommendations&quot;</span>) <span class="sc">+</span></span>
<span id="cb28-27"><a href="#cb28-27" tabindex="-1"></a>        <span class="fu">coord_fixed</span>(<span class="at">expand =</span> F) <span class="sc">+</span></span>
<span id="cb28-28"><a href="#cb28-28" tabindex="-1"></a>        <span class="fu">theme_classic</span>(<span class="at">base_size =</span> <span class="dv">15</span>) <span class="sc">+</span></span>
<span id="cb28-29"><a href="#cb28-29" tabindex="-1"></a>        <span class="fu">scale_fill_brewer</span>(<span class="st">&quot;&quot;</span>, <span class="at">type =</span> <span class="st">&quot;seq&quot;</span>, <span class="at">palette =</span> <span class="st">&quot;Reds&quot;</span>, <span class="at">direction =</span> <span class="dv">1</span>,</span>
<span id="cb28-30"><a href="#cb28-30" tabindex="-1"></a>                          <span class="at">breaks =</span> <span class="dv">0</span><span class="sc">:</span><span class="dv">1</span>, <span class="at">label =</span> <span class="fu">c</span>(<span class="st">&quot;Do not follow&quot;</span>, <span class="st">&quot;Follow&quot;</span>)) <span class="sc">+</span></span>
<span id="cb28-31"><a href="#cb28-31" tabindex="-1"></a>        <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>, <span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>)) <span class="sc">+</span></span>
<span id="cb28-32"><a href="#cb28-32" tabindex="-1"></a>        <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> n), <span class="at">color =</span> <span class="st">&quot;grey10&quot;</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
<p>We can also consider policy class with a decreasing monotonicity
constraint.</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" tabindex="-1"></a>nca_follow_policy_dec <span class="ot">&lt;-</span> <span class="fu">get_monotone_policy_ai</span>(<span class="at">X =</span> psaX, <span class="at">wts =</span> nca_wts, <span class="at">rev =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">get_policy_value</span>(nca_follow_policy_dec, <span class="at">n_obs =</span> <span class="fu">length</span>(Y))</span>
<span id="cb30-2"><a href="#cb30-2" tabindex="-1"></a><span class="co">#&gt; [1] 0</span></span></code></pre></div>
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